DIGAT: Modeling News Recommendation with Dual-Graph Interaction
Zhiming Mao, Jian Li, Hongru Wang, Xingshan Zeng, Kam-Fai Wong
Abstract
News recommendation (NR) is essential for online news services. Existing NR methods typically adopt a news-user representation learning framework, facing two potential limitations. First, in news encoder, single candidate news encoding suffers from an insufficient semantic information problem. Second, existing graph-based NR methods are promising but lack effective news-user feature interaction, rendering the graph-based recommendation suboptimal. To overcome these limitations, we propose dual-interactive graph attention networks (DIGAT) consisting of news- and user-graph channels. In the news-graph channel, we enrich the semantics of single candidate news by incorporating the semantically relevant news information with a semantic-augmented graph (SAG). In the user-graph channel, multi-level user interests are represented with a news-topic graph. Most notably, we design a dual-graph interaction process to perform effective feature interaction between the news and user graphs, which facilitates accurate news-user representation matching. Experiment results on the benchmark dataset MIND show that DIGAT outperforms existing news recommendation methods. Further ablation studies and analyses validate the effectiveness of (1) semantic-augmented news graph modeling and (2) dual-graph interaction.- Anthology ID:
- 2022.findings-emnlp.491
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6595–6607
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.491
- DOI:
- 10.18653/v1/2022.findings-emnlp.491
- Cite (ACL):
- Zhiming Mao, Jian Li, Hongru Wang, Xingshan Zeng, and Kam-Fai Wong. 2022. DIGAT: Modeling News Recommendation with Dual-Graph Interaction. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6595–6607, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- DIGAT: Modeling News Recommendation with Dual-Graph Interaction (Mao et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2022.findings-emnlp.491.pdf